An Aligned Multi-Temporal Multi-Resolution Satellite Image Dataset for Change Detection Research

This paper presents an aligned multi-temporal and multi-resolution satellite image dataset for research in change detection. We expect our dataset to be useful to researchers who want to fuse information from multiple satellites for detecting changes on the surface of the earth that may not be fully visible in any single satellite. The dataset we present was created by augmenting the SpaceNet-7 dataset with temporally parallel stacks of Landsat and Sentinel images. The SpaceNet-7 dataset consists of time-sequenced Planet images recorded over 101 AOIs (Areas-of-Interest). In our dataset, for each of the 60 AOIs that are meant for training, we augment the Planet datacube with temporally parallel datacubes of Landsat and Sentinel images. The temporal alignments between the high-res Planet images, on the one hand, and the Landsat and Sentinel images, on the other, are approximate since the temporal resolution for the Planet images is one month -- each image being a mosaic of the best data collected over a month. Whenever we have a choice regarding which Landsat and Sentinel images to pair up with the Planet images, we have chosen those that had the least cloud cover. A particularly important feature of our dataset is that the high-res and the low-res images are spatially aligned together with our MuRA framework presented in this paper. Foundational to the alignment calculation is the modeling of inter-satellite misalignment errors with polynomials as in NASA's AROP algorithm. We have named our dataset MuRA-T for the MuRA framework that is used for aligning the cross-satellite images and "T" for the temporal dimension in the dataset.

maskGRU: Tracking Small Objects in the Presence of Large Background Motions

We propose a recurrent neural network-based spatio-temporal framework named maskGRU for the detection and tracking of small objects in videos. While there have been many developments in the area of object tracking in recent years, tracking a small moving object amid other moving objects and actors (such as a ball amid moving players in sports footage) continues to be a difficult task. Existing spatio-temporal networks, such as convolutional Gated Recurrent Units (convGRUs), are difficult to train and have trouble accurately tracking small objects under such conditions. To overcome these difficulties, we developed the maskGRU framework that uses a weighted sum of the internal hidden state produced by a convGRU and a 3-channel mask of the tracked object's predicted bounding box as the hidden state to be used at the next time step of the underlying convGRU. We believe the technique of incorporating a mask into the hidden state through a weighted sum has two benefits: controlling the effect of exploding gradients and introducing an attention-like mechanism into the network by indicating where in the previous video frame the object is located. Our experiments show that maskGRU outperforms convGRU at tracking objects that are small relative to the video resolution even in the presence of other moving objects.